Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.1 KiB
Average record size in memory80.2 B

Variable types

Numeric6
Categorical4

Alerts

X1 is highly overall correlated with X2 and 4 other fieldsHigh correlation
X2 is highly overall correlated with X1 and 4 other fieldsHigh correlation
X3 is highly overall correlated with X4 and 1 other fieldsHigh correlation
X4 is highly overall correlated with X1 and 5 other fieldsHigh correlation
X5 is highly overall correlated with X1 and 5 other fieldsHigh correlation
X7 is highly overall correlated with X8 and 1 other fieldsHigh correlation
X8 is highly overall correlated with X7High correlation
Y1 is highly overall correlated with X1 and 5 other fieldsHigh correlation
Y2 is highly overall correlated with X1 and 4 other fieldsHigh correlation
X5 is uniformly distributed Uniform
X6 is uniformly distributed Uniform
X8 has 48 (6.2%) zeros Zeros

Reproduction

Analysis started2025-07-11 03:28:03.945637
Analysis finished2025-07-11 03:28:06.142450
Duration2.2 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

X1
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76416667
Minimum0.62
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-07-10T22:28:06.166197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.62
5-th percentile0.62
Q10.6825
median0.75
Q30.83
95-th percentile0.98
Maximum0.98
Range0.36
Interquartile range (IQR)0.1475

Descriptive statistics

Standard deviation0.10577748
Coefficient of variation (CV)0.138422
Kurtosis-0.70656754
Mean0.76416667
Median Absolute Deviation (MAD)0.08
Skewness0.49551251
Sum586.88
Variance0.011188874
MonotonicityNot monotonic
2025-07-10T22:28:06.203554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.98 64
8.3%
0.9 64
8.3%
0.86 64
8.3%
0.82 64
8.3%
0.79 64
8.3%
0.76 64
8.3%
0.74 64
8.3%
0.71 64
8.3%
0.69 64
8.3%
0.66 64
8.3%
Other values (2) 128
16.7%
ValueCountFrequency (%)
0.62 64
8.3%
0.64 64
8.3%
0.66 64
8.3%
0.69 64
8.3%
0.71 64
8.3%
0.74 64
8.3%
0.76 64
8.3%
0.79 64
8.3%
0.82 64
8.3%
0.86 64
8.3%
ValueCountFrequency (%)
0.98 64
8.3%
0.9 64
8.3%
0.86 64
8.3%
0.82 64
8.3%
0.79 64
8.3%
0.76 64
8.3%
0.74 64
8.3%
0.71 64
8.3%
0.69 64
8.3%
0.66 64
8.3%

X2
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.70833
Minimum514.5
Maximum808.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-07-10T22:28:06.237242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum514.5
5-th percentile514.5
Q1606.375
median673.75
Q3741.125
95-th percentile808.5
Maximum808.5
Range294
Interquartile range (IQR)134.75

Descriptive statistics

Standard deviation88.086116
Coefficient of variation (CV)0.13113745
Kurtosis-1.0594542
Mean671.70833
Median Absolute Deviation (MAD)73.5
Skewness-0.12513088
Sum515872
Variance7759.1638
MonotonicityNot monotonic
2025-07-10T22:28:06.268408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
514.5 64
8.3%
563.5 64
8.3%
588 64
8.3%
612.5 64
8.3%
637 64
8.3%
661.5 64
8.3%
686 64
8.3%
710.5 64
8.3%
735 64
8.3%
759.5 64
8.3%
Other values (2) 128
16.7%
ValueCountFrequency (%)
514.5 64
8.3%
563.5 64
8.3%
588 64
8.3%
612.5 64
8.3%
637 64
8.3%
661.5 64
8.3%
686 64
8.3%
710.5 64
8.3%
735 64
8.3%
759.5 64
8.3%
ValueCountFrequency (%)
808.5 64
8.3%
784 64
8.3%
759.5 64
8.3%
735 64
8.3%
710.5 64
8.3%
686 64
8.3%
661.5 64
8.3%
637 64
8.3%
612.5 64
8.3%
588 64
8.3%

X3
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean318.5
Minimum245
Maximum416.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-07-10T22:28:06.297813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum245
5-th percentile245
Q1294
median318.5
Q3343
95-th percentile416.5
Maximum416.5
Range171.5
Interquartile range (IQR)49

Descriptive statistics

Standard deviation43.626481
Coefficient of variation (CV)0.13697482
Kurtosis0.11659327
Mean318.5
Median Absolute Deviation (MAD)24.5
Skewness0.53341749
Sum244608
Variance1903.2699
MonotonicityNot monotonic
2025-07-10T22:28:06.331101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
294 192
25.0%
318.5 192
25.0%
343 128
16.7%
416.5 64
 
8.3%
245 64
 
8.3%
269.5 64
 
8.3%
367.5 64
 
8.3%
ValueCountFrequency (%)
245 64
 
8.3%
269.5 64
 
8.3%
294 192
25.0%
318.5 192
25.0%
343 128
16.7%
367.5 64
 
8.3%
416.5 64
 
8.3%
ValueCountFrequency (%)
416.5 64
 
8.3%
367.5 64
 
8.3%
343 128
16.7%
318.5 192
25.0%
294 192
25.0%
269.5 64
 
8.3%
245 64
 
8.3%

X4
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
220.5
384 
147.0
192 
122.5
128 
110.25
64 

Length

Max length6
Median length5
Mean length5.0833333
Min length5

Characters and Unicode

Total characters3904
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110.25
2nd row110.25
3rd row110.25
4th row110.25
5th row122.5

Common Values

ValueCountFrequency (%)
220.5 384
50.0%
147.0 192
25.0%
122.5 128
 
16.7%
110.25 64
 
8.3%

Length

2025-07-10T22:28:06.370341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T22:28:06.404862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
220.5 384
50.0%
147.0 192
25.0%
122.5 128
 
16.7%
110.25 64
 
8.3%

Most occurring characters

ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3136
80.3%
Other Punctuation 768
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1088
34.7%
0 640
20.4%
5 576
18.4%
1 448
14.3%
4 192
 
6.1%
7 192
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

X5
Categorical

High correlation  Uniform 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
7.0
384 
3.5
384 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row7.0
3rd row7.0
4th row7.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.0 384
50.0%
3.5 384
50.0%

Length

2025-07-10T22:28:06.438845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T22:28:06.460491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.0 384
50.0%
3.5 384
50.0%

Most occurring characters

ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1536
66.7%
Other Punctuation 768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 384
25.0%
0 384
25.0%
3 384
25.0%
5 384
25.0%
Other Punctuation
ValueCountFrequency (%)
. 768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

X6
Categorical

Uniform 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2
192 
3
192 
4
192 
5
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row5
5th row2

Common Values

ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Length

2025-07-10T22:28:06.599786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T22:28:06.624729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring characters

ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

X7
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0.1
240 
0.25
240 
0.4
240 
0.0
48 

Length

Max length4
Median length3
Mean length3.3125
Min length3

Characters and Unicode

Total characters2544
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.1 240
31.2%
0.25 240
31.2%
0.4 240
31.2%
0.0 48
 
6.2%

Length

2025-07-10T22:28:06.668169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T22:28:06.701550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.1 240
31.2%
0.25 240
31.2%
0.4 240
31.2%
0.0 48
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1776
69.8%
Other Punctuation 768
30.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 816
45.9%
1 240
 
13.5%
2 240
 
13.5%
5 240
 
13.5%
4 240
 
13.5%
Other Punctuation
ValueCountFrequency (%)
. 768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

X8
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8125
Minimum0
Maximum5
Zeros48
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-07-10T22:28:06.728237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.5509597
Coefficient of variation (CV)0.55145233
Kurtosis-1.1487088
Mean2.8125
Median Absolute Deviation (MAD)1
Skewness-0.088689175
Sum2160
Variance2.4054759
MonotonicityNot monotonic
2025-07-10T22:28:06.766181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 144
18.8%
2 144
18.8%
3 144
18.8%
4 144
18.8%
5 144
18.8%
0 48
 
6.2%
ValueCountFrequency (%)
0 48
 
6.2%
1 144
18.8%
2 144
18.8%
3 144
18.8%
4 144
18.8%
5 144
18.8%
ValueCountFrequency (%)
5 144
18.8%
4 144
18.8%
3 144
18.8%
2 144
18.8%
1 144
18.8%
0 48
 
6.2%

Y1
Real number (ℝ)

High correlation 

Distinct586
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.307201
Minimum6.01
Maximum43.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-07-10T22:28:06.819542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.01
5-th percentile10.4635
Q112.9925
median18.95
Q331.6675
95-th percentile39.86
Maximum43.1
Range37.09
Interquartile range (IQR)18.675

Descriptive statistics

Standard deviation10.090196
Coefficient of variation (CV)0.45232909
Kurtosis-1.2455719
Mean22.307201
Median Absolute Deviation (MAD)7.515
Skewness0.36044889
Sum17131.93
Variance101.81205
MonotonicityNot monotonic
2025-07-10T22:28:06.871290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.16 6
 
0.8%
13 5
 
0.7%
15.55 4
 
0.5%
10.68 4
 
0.5%
28.15 4
 
0.5%
15.23 4
 
0.5%
15.09 4
 
0.5%
12.93 4
 
0.5%
14.6 4
 
0.5%
32.31 4
 
0.5%
Other values (576) 725
94.4%
ValueCountFrequency (%)
6.01 1
0.1%
6.04 1
0.1%
6.05 1
0.1%
6.07 1
0.1%
6.37 2
0.3%
6.4 2
0.3%
6.77 1
0.1%
6.79 1
0.1%
6.81 1
0.1%
6.85 1
0.1%
ValueCountFrequency (%)
43.1 1
0.1%
42.96 1
0.1%
42.77 1
0.1%
42.74 1
0.1%
42.62 1
0.1%
42.5 1
0.1%
42.49 1
0.1%
42.11 1
0.1%
42.08 1
0.1%
41.96 1
0.1%

Y2
Real number (ℝ)

High correlation 

Distinct636
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.58776
Minimum10.9
Maximum48.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-07-10T22:28:06.926880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.9
5-th percentile13.6175
Q115.62
median22.08
Q333.1325
95-th percentile40.037
Maximum48.03
Range37.13
Interquartile range (IQR)17.5125

Descriptive statistics

Standard deviation9.5133056
Coefficient of variation (CV)0.38691224
Kurtosis-1.1471903
Mean24.58776
Median Absolute Deviation (MAD)7.54
Skewness0.39599247
Sum18883.4
Variance90.502983
MonotonicityNot monotonic
2025-07-10T22:28:06.986197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.33 4
 
0.5%
29.79 4
 
0.5%
14.27 4
 
0.5%
17.2 4
 
0.5%
14.28 4
 
0.5%
13.65 3
 
0.4%
14.61 3
 
0.4%
16.9 3
 
0.4%
13.79 3
 
0.4%
15.44 3
 
0.4%
Other values (626) 733
95.4%
ValueCountFrequency (%)
10.9 1
0.1%
10.94 1
0.1%
11.17 1
0.1%
11.19 1
0.1%
11.27 1
0.1%
11.29 1
0.1%
11.67 1
0.1%
11.72 1
0.1%
11.73 1
0.1%
11.74 1
0.1%
ValueCountFrequency (%)
48.03 1
0.1%
47.59 1
0.1%
47.01 1
0.1%
46.94 1
0.1%
46.44 1
0.1%
46.23 1
0.1%
45.97 1
0.1%
45.59 1
0.1%
45.52 1
0.1%
45.48 1
0.1%

Interactions

2025-07-10T22:28:05.797345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.535537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.774146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.071432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.326921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.563430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.840096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.573488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.824441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.119185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.364506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.603922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.880449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.608723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.878350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.170379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.403718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.643922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.931960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.648720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.927032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.207916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.441060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.681735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.973195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.685895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.973457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.244893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.476819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.721724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:06.013356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:04.727661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.025402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.285399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.519138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-10T22:28:05.760794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-10T22:28:07.030787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
X1X2X3X4X5X6X7X8Y1Y2
X11.000-1.000-0.2560.9390.9080.0000.0000.0000.6220.651
X2-1.0001.0000.2560.9230.9950.0000.0000.000-0.622-0.651
X3-0.2560.2561.0000.5960.6180.0000.0000.0000.4710.416
X40.9390.9230.5961.0000.9990.0000.0000.0000.6230.629
X50.9080.9950.6180.9991.0000.0000.0000.0000.9600.963
X60.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
X70.0000.0000.0000.0000.0000.0001.0000.5730.5330.359
X80.0000.0000.0000.0000.0000.0000.5731.0000.0680.046
Y10.622-0.6220.4710.6230.9600.0000.5330.0681.0000.973
Y20.651-0.6510.4160.6290.9630.0000.3590.0460.9731.000

Missing values

2025-07-10T22:28:06.072326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-10T22:28:06.113612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

X1X2X3X4X5X6X7X8Y1Y2
00.98514.5294.0110.257.020.0015.5521.33
10.98514.5294.0110.257.030.0015.5521.33
20.98514.5294.0110.257.040.0015.5521.33
30.98514.5294.0110.257.050.0015.5521.33
40.90563.5318.5122.507.020.0020.8428.28
50.90563.5318.5122.507.030.0021.4625.38
60.90563.5318.5122.507.040.0020.7125.16
70.90563.5318.5122.507.050.0019.6829.60
80.86588.0294.0147.007.020.0019.5027.30
90.86588.0294.0147.007.030.0019.9521.97
X1X2X3X4X5X6X7X8Y1Y2
7580.66759.5318.5220.53.540.4514.9217.55
7590.66759.5318.5220.53.550.4515.1618.06
7600.64784.0343.0220.53.520.4517.6920.82
7610.64784.0343.0220.53.530.4518.1920.21
7620.64784.0343.0220.53.540.4518.1620.71
7630.64784.0343.0220.53.550.4517.8821.40
7640.62808.5367.5220.53.520.4516.5416.88
7650.62808.5367.5220.53.530.4516.4417.11
7660.62808.5367.5220.53.540.4516.4816.61
7670.62808.5367.5220.53.550.4516.6416.03